Deep Convolutional Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve problems of character recognition, but their use has become as widespread as it is now thanks to recent researches. These CNNs have won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolutional neural network became a very useful tool in the field of the machine learning.
Such CNNs are also used in a field of autonomous driving. The CNNs are responsible for image processing such as semantic segmentation, object detection, and free space detection in autonomous vehicles.
Recently, a plurality of cameras has been used in order to further improve a stability of the autonomous vehicles. Herein, it is important to use the images in a coordinated way which are obtained through the plurality of cameras in order to reduce a redundancy of computation and to grasp surrounding space more clearly. Particularly, during the coordination of the images, parts of ROIs, which are areas in which objects are estimated as located in each image, often overlap with one another among different images. Therefore it is crucial to integrate information on such ROIs.
As a conventional technique for such a purpose, a non-maximal suppression is used. That is, an overlapping ratio between bounding boxes including objects of a same class is calculated, and if the ratio is equal to or greater than a threshold, the bounding boxes are combined with each other. The problem with the conventional technique is that if the threshold is too low, the bounding boxes that are not related to one another will be merged, and if the threshold is too high, the bounding boxes to be merged will not be integrated with one another, thus it is difficult to determine the threshold, and the threshold must be constantly updated as the case may be.